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[Breaking] Change default evaluation metric for classification to logloss / mlogloss (#6183)

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* Change DefaultEvalMetric of classification from error to logloss

* Change default binary metric in plugin/example/custom_obj.cc

* Set old error metric in python tests

* Set old error metric in R tests

* Fix missed eval metrics and typos in R tests

* Fix setting eval_metric twice in R tests

* Add warning for empty eval_metric for classification

* Fix Dask tests

Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Christian Lorentzen
2020-10-02 21:06:47 +02:00
committed by GitHub
parent e0e4f15d0e
commit cf4f019ed6
18 changed files with 56 additions and 32 deletions
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5
tests/python/test_with_dask.py
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@@ -274,7 +274,7 @@ def test_dask_classifier():
X, y = generate_array()
y = (y * 10).astype(np.int32)
classifier = xgb.dask.DaskXGBClassifier(
verbosity=1, n_estimators=2)
verbosity=1, n_estimators=2, eval_metric='merror')
classifier.client = client
classifier.fit(X, y, eval_set=[(X, y)])
prediction = classifier.predict(X)
@@ -386,6 +386,7 @@ def run_empty_dmatrix_cls(client, parameters):
y = dd.from_array(np.random.randint(low=0, high=n_classes, size=kRows))
dtrain = xgb.dask.DaskDMatrix(client, X, y)
parameters['objective'] = 'multi:softprob'
parameters['eval_metric'] = 'merror'
parameters['num_class'] = n_classes
out = xgb.dask.train(client, parameters,
@@ -482,7 +483,7 @@ async def run_dask_classifier_asyncio(scheduler_address):
X, y = generate_array()
y = (y * 10).astype(np.int32)
classifier = await xgb.dask.DaskXGBClassifier(
verbosity=1, n_estimators=2)
verbosity=1, n_estimators=2, eval_metric='merror')
classifier.client = client
await classifier.fit(X, y, eval_set=[(X, y)])
prediction = await classifier.predict(X)